The problem of missing data arises frequently in a wide range of fields, including biomedical studies, social science, engineering, economics, and computer science. Statistical inference in the presence of missing observations has been well studied in classical statistics. See, e.g., Ibrahim and Molenberghs [1]} for a review of missing data methods in longitudinal studies and Schafer [2]} for literature on handling multivariate data with missing observations. See Little and Rubin [3]} and the references therein for a comprehensive treatment of missing data problems.
